Graph Neural Networks: Essentials and Use Cases

This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals and how they complement advances in generative, agentic, and physical AI this book is an essential resource for understanding and leveraging their potential.
The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation.
Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains.
GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged:

• Graph Convolutional Networks (GCNs): These networks learn from a node s local neighborhood by aggregating information from adjacent nodes, updating the node s representation in the process.
• Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.
• Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data.

GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.

List of contents

Chapter 1 Applications of Graph Neural Networks in Computer Vision: Transforming Perception Through Structure.- Chapter 2 Large-Scale Learnable Graph Convolutional Networks (LGCNs) Graph Neural Networks (GNNs): Articulating the Advancements and Advantages, Graph Neural Networks (GNNs): the Essentials and the Use Cases.- Chapter 3 Deep Finance: Harnessing Graph-Based Neural Networks for Market Predictions.- Chapter 4 Illustrating the significance of graphical neural networks to prevent social engineering.- Chapter 5 The Challenges of Graph Neural Networks.- Chapter 6 Utilizing Graph Neural Networks (GNN) in Quantum-Natural Language Processing (Q-NLP) for Risk Management in Banking Sector: A Novel Approach.- Chapter 7 Graph Neural Networks (GNNs) Applications.- Chapter 8 Revolutionizing AI: Applications of Graph Neural Networks Across Industries.- Chapter 9 Graph Neural Networks for Enhanced Computer Vision.- Chapter 10 Enhancing Natural Language Processing with Graph Neural Networks: Improving Understanding, Performance, and Transparency.- Chapter 11 Microarray Data Feature Selection and Classification Using Graph Neural Networks.- Chapter 12 Graph Neural Network.- Chapter 13 Graph Neural Networks for Scene Understanding: A
Review and Future Directions.- Chapter 14 Graph Neural Networks: Architecting Intelligence Across Domains - From Molecular Design to Urban Systems.- Chapter 15 Unlocking Insights: A Guide to the Benefits of Graph-represented Data In Gnns.- Chapter 16 Graph Neural Networks Applications - A Dwell into Biomedical Applications and Traffic Flow Analysis.- Index.

About the author

Pethuru Raj is chief architect in the Edge AI division of Reliance Jio Platforms Ltd., Bangalore, India. He focuses on emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI) Model Optimization, Big and Streaming Data Analytics, Blockchain, Digital Twins, Cloud-native, Serverless and Edge Computing, Reliability Engineering, Microservices architecture (MSA), Event-driven Architecture (EDA), 5G/6G and Metaverse. He previously worked at IBM Global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has published more than thirty research papers in peer-reviewed journals, authored and edited forty-two books, and contributed more than fifty book chapters. He is a professional member of ACM. He received his PhD degree from Anna University, Chennai, India and did his postdoctoral research in computer science and automation (CSA) at the Indian Institute of Science, Bangalore, and at the Nagoya Institute of Technology, Kyoto University and the University of Tsukuba, Japan.
Dr Pawan Whig Dr Pawan Whig Senior IEEE Member, Honorary Fellow Member Threws India Dr Pawan Whig is a highly accomplished professional with a diverse skill set and a rich educational background. He holds a B.Tech in Electronics and Communication Engineering, which he completed in 2005, followed by an M.Tech in VLSI in 2008. He further expanded his expertise by earning a Doctorate from Jamia Millia Islamia. With over 23 years of experience in the field of Electronics and Communication, Dr Whig has made significant contributions to academia and industry. In addition to his core specialization, Dr. Whig is also well-versed in the areas of Artificial Intelligence, Machine Learning, and IoT. With over a decade of experience in these fields, he has played a pivotal role in the intersection of Electronics and Communication Engineering with AI, ML, and IoT technologies. His research interests encompass a wide range of areas, including Analog Signal Processing, Sensor Modeling, Water Quality Monitoring Applications, Simulation & Design, as well as AI, ML, and IoT. Dr Whig has published numerous technical articles in over 120 national and international journals, solidifying his position as a respected researcher and thought leader. Dr. Whig is an active member of the International Association of Engineers in Hong Kong, ISTE, IEEE, SCI, IEI, and the Computer Society of India (CSI).
N. Susila is Dean, Emerging Technology, at Sri Krishna College of Engineering and Technology with a total experience of 23 yrs. She completed her PhD in the field of Information and Communication Engineering in Cloud Computing. She has published over 30 papers in national and international journals and conferences and published 5 patents. She is an Innovation Ambassador of the Institution Innovation Council, Ministry of Education and a NITTTR mentor. She has received various awards and to name a few, National Cloud Champion Coordinator by AWS Educate, National Winner Coordinator, Learnathon, Best Coordinator, Skill Edge Technological Foundation Courses. She has authored various books and book chapters in the reputed press like Elsevier, CRC Press, IET, IGI Global etc.  She has received grants from various funding agencies like AICTE, ICMR and DXC Technologies for organizing National and International Level Conferences, Workshops and the Women Empowerment Program. She has mentored a project titled “INTELLBOT” for the 2000 – 2004 batch students, which won First Prize at the Indian National Academy of Engineering (INAE), Delhi.
S. Usha is Professor and Head, Dean R&I, CSE, RRCE with an experience of 27 years. She received her Ph.D degree from Sathyabama University in Mobile Ad Hoc Networks. She has 70+ publications in International and National conferences, 45+ publications in national journals and international journals in Mobile Ad hoc Networks, IoT, and wireless security. She received the woman researcher award from Elsevier in the year 2020. She has reviewed papers in Springer journals, as well as IGI Global, IJCs and CiiT journals. She has authored two books on operating systems and C programming for problem solving,  and co-edited Applied Learning Algorithms for Intelligent IoT (Routledge, 2022).
Dr Nikhitha Yathiraju received a PhD in Information Technology from the University of the Cumberlands, USA. Her research work was in the study of Artificial Intelligence in Cloud Enterprise Resource Planning. She is interested in writing journals and articles on Cyberthreats in Cloud technologies and Social Engineering Techniques. She was a certified Salesforce Administrator and works as Lead Automation Engineer in California, USA. She also worked as a Teaching Assistant at Silicon Valley University. She was honoured with the” International Best Researcher” award by ISSN-IIRAC 2023 in her field of expertise. She was also honoured with the “Best researcher award in AI” from International Research and Development Centre for Publication in 2022. She also participated in various international conferences like IRDCP, IFERP etc. She was also a member of Sigma Xi, SEFM and the American Educational Research Association (AERA). She participated as a Keynote speaker for various conferences like Wicked6, threwstalk.

Product details

• Assisted by: Pethuru Raj Chelliah (Editor), Susila Nagarajan (Editor), Susila Nagarajan et al (Editor), Usha Sakthivel (Editor), Pawan Whig (Editor), Nikhitha Yathiraju (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 11.08.2025
• EAN: 9783031885372
• ISBN: 978-3-0-3188537-2
• No. of pages: 392
• Illustrations: IV, 392 p. 74 illus., 64 illus. in color.
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, Maschinelles Lernen, Artificial Intelligence, Kombinatorik und Graphentheorie, Natürliche Sprachen und maschinelle Übersetzung, Maschinelles Sehen, Bildverstehen, Computer Vision, Graph Theory, Natural Language Processing (NLP), Graph convolutional networks, Graph analytics, Graph Data Science, Graph Transformer Networks, Graph Isomorphism Networks, GNN